Title
V1
Name & student number
2019-11-29
# load libraries
library(dplyr)
library(readxl)
library(janitor)
library(ggplot2)
library(skimr)
library(stringr)
library(maps)
library(mapdata)
library(plotly)
library(sf)
library(ggthemes)
library(ggpubr)
Sys.setenv('MAPBOX_TOKEN' = 'pk.eyJ1IjoiYXJpa2IwIiwiYSI6ImNrM2V5djl2cDAwbzkzZG5sMG16ZndnbmYifQ.BfoEKQZ2gykEJvFmt1xe8Q')Description
Headline
The following plot….
listings <- read.csv("data/listings-summarized.csv")
neighbourhoods <- read.csv("data/neighbourhoods.csv")
all_listings <-read.csv("data/listings.csv")
ottawawards <- read_sf("data/Wards.shp")
ditch_the_axes <- theme(
axis.text = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.title = element_blank(),
panel.background = element_rect(fill = "white", colour = "white")
)
ottawawards$WARD_NAME_[ottawawards$WARD_NAME_ == "Orléans"] <- "Orleans"
ottawawards$WARD_NAME_[ottawawards$WARD_NAME_ == "West Carleton – March"] <- "West Carleton-March"
ottawawards$WARD_NAME_[ottawawards$WARD_NAME_ == "Stittsville"] <- "Stittsville-Kanata West"
ottawawards <- ottawawards %>%
left_join(count(listings,neighbourhood),c("WARD_NAME_"="neighbourhood"))
ottawawards %>% ggplot() +
geom_sf(aes(fill=n),color = "white") +
scale_fill_gradient(low = "light blue", high = "dark blue") +
ditch_the_axes
df_listing_sum <- listings %>%
group_by(neighbourhood, room_type) %>%
summarize(sum = n()) %>%
group_by(neighbourhood) %>%
mutate(total=sum(sum)) %>%
mutate(percent=total/nrow(listings))
df_listing_sum %>%
ggplot(aes(x=reorder(neighbourhood,total),y=sum,fill=room_type)) +
geom_bar(stat="identity") +
coord_flip() +
theme_minimal()
p <- listings %>%
plot_mapbox(lat = ~latitude, lon = ~longitude,
split = ~room_type, size=2,
mode = 'scattermapbox', hoverinfo="price") %>%
layout(mapbox = list(zoom = 9,
center = list(lat = ~median(latitude),
lon = ~median(longitude))))
pcal <- read.csv("data/calendar.csv") %>%
filter(as.Date(date) < as.Date("2019-11-01")) %>%
filter(available =="f")
reviews <- read.csv("data/reviews.csv") %>%
filter(as.Date(date) > as.Date("2019-01-01"))
rev <- reviews %>% count(listing_id)
#getting rid of not active listings, i.e havent been reviewd since hte beging of the year and have less than 1 review per month
df_activity <- listings %>% filter(number_of_reviews != 0) %>%
filter(as.Date(last_review) > as.Date("2018-12-31")) %>%
select(id,neighbourhood,minimum_nights,price,reviews_per_month,number_of_reviews,room_type) %>%
mutate(
min_price_per_month = if_else(
#((minimum_nights > 30) & (reviews_per_month > 0.8)),
minimum_nights * reviews_per_month > 30,
price * minimum_nights / 30,
reviews_per_month * price * minimum_nights
)
)
df_minimum_activity <- df_activity %>%
group_by(neighbourhood,room_type) %>%
summarise(average=mean(min_price_per_month))
df_minimum_activity %>% ggplot(aes(x=reorder(neighbourhood,average),y=average,fill=room_type)) +
geom_bar(stat="identity",position = "dodge") +
coord_flip() +
theme_minimal()
generateGraphMinPrice <- function(name,colour) {
df_minimum_activity %>% filter(room_type==name) %>%
ggplot(aes(x=reorder(neighbourhood,average),y=average,fill=room_type)) +
geom_bar(stat="identity",position = "dodge",fill=colour) +
coord_flip() +
theme_minimal()
}
a <- generateGraphMinPrice("Entire home/apt","red")
b <- generateGraphMinPrice("Hotel room","green")
c <- generateGraphMinPrice("Private room","cyan")
d <- generateGraphMinPrice("Shared room","blue")
#df_amount_listings <- listings %>% distinct(host_id,calculated_host_listings_count)%>% filter(calculated_host_listings_count > 2)
ggarrange(a,b,c,d+rremove("x.text"),ncol =2,nrow = 2,labels=c("Entire home/apt","Hotel room","Private room","Shared room"),common.legend = TRUE,legend = "bottom")
Explanation of the plot…
Suggestions for further explorations
- Next steps, etc
References
- Links to any sources used, etc
Appendix: sessionInfo
sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.2.3 magrittr_1.5 ggthemes_4.2.0 sf_0.8-0
## [5] plotly_4.9.1 mapdata_2.3.0 maps_3.3.0 stringr_1.4.0
## [9] skimr_1.0.7 ggplot2_3.2.1 janitor_1.2.0 readxl_1.3.1
## [13] dplyr_0.8.3
##
## loaded via a namespace (and not attached):
## [1] tidyselect_0.2.5 xfun_0.9 purrr_0.3.2
## [4] colorspace_1.4-1 vctrs_0.2.0 htmltools_0.4.0
## [7] viridisLite_0.3.0 yaml_2.2.0 rlang_0.4.0
## [10] later_1.0.0 e1071_1.7-2 pillar_1.4.2
## [13] glue_1.3.1 withr_2.1.2 DBI_1.0.0
## [16] lifecycle_0.1.0 munsell_0.5.0 ggsignif_0.6.0
## [19] gtable_0.3.0 cellranger_1.1.0 htmlwidgets_1.5.1
## [22] evaluate_0.14 labeling_0.3 knitr_1.24
## [25] fastmap_1.0.1 httpuv_1.5.2 crosstalk_1.0.0
## [28] class_7.3-15 Rcpp_1.0.2 xtable_1.8-4
## [31] KernSmooth_2.23-15 promises_1.1.0 scales_1.0.0
## [34] backports_1.1.4 classInt_0.4-2 jsonlite_1.6
## [37] mime_0.7 digest_0.6.20 stringi_1.4.3
## [40] shiny_1.4.0 cowplot_1.0.0 grid_3.6.1
## [43] tools_3.6.1 lazyeval_0.2.2 tibble_2.1.3
## [46] crayon_1.3.4 tidyr_1.0.0 pkgconfig_2.0.2
## [49] zeallot_0.1.0 data.table_1.12.6 assertthat_0.2.1
## [52] rmarkdown_1.15 httr_1.4.1 R6_2.4.0
## [55] units_0.6-5 compiler_3.6.1
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